Optimize operations / Legacy Optimization

Maintain legacy systems with AI-backed release confidence

S-PRO turns legacy maintenance into a governed modernization workflow: documentation analysis, continuous health monitoring, release testing and AI-assisted fix workflows that help teams ship safely without endless manual regression.

Discuss legacy modernization Back to offering

4Core capabilities across the testing workflow

A-EHealth ratings for maintainability, reliability and security

1 FTETarget operating model after pilot handover

Legacy optimization / health dashboard

plant-operations-consoleLast scan 09:42

BMaintainability

AReliability

DSecurity

312Issues detected4 blockers · 23 critical · 87 filesPrioritize

38%Test coverage12% gap to target across modulesGap

47VulnerabilitiesDependency and code health blockersRisk

Product screens

A visual operating layer for legacy maintenance

The product page converts the ABB collaboration deck into screens buyers can understand: system onboarding, health dashboard, release testing and AI fix workflow.

documentation analysis

System decompositionFit-gap

78%Asset ManagementMetadata, lifecycle states, namingPass

62%Alarm & Event Management5 subcomponents expandedGap

25%Audit loggingRetention and immutability driftDrift

Documentation analysisParse specs, Confluence, source code and manual tests into modules, journeys and coverage gaps.

health monitoring

Code healthMain branch

487kLines of code

23Modules

1,247Code smells

Technical debt

Languages

Health monitoringMaintainability, reliability, security, coverage and drift in one executive-grade view.

release testing

Release pipelineCI/CD

Static analysisSecurity and dependency scan completed in CI

E2E regressionAgent found critical alarm escalation failure

Doc driftNotification engine spec is 6 months out of sync

Rollout gateProduction rollout blocked until fix validates

Release testingEvery release branch runs regression, performance, compatibility, security and spec validation.

AI fix workflow

Blocker AC-011Human approved gates

Plan

Identify failure source and repair approach.

Branch

Create branch and link tickets.

Implement

Modify files and update tests.

Validate

Run E2E, static analysis and security.

PR

Open pull request for review.

AI workflow orchestrationThe agent drafts plan, code and validation; humans approve at every gate.

Core capabilities

Four capabilities cover the current testing workflow

Manual testing often burns the most time and budget every release. Legacy modernization turns repetitive regression, code health, security and fix workflow into an automated operating system.

Documentation analysis

Upload specs, Confluence pages, source code, tests and Jira tickets. The system builds a structured map of modules, components and journeys.

Health monitoring

Scan every commit for code quality, coverage, vulnerabilities and drift, then roll signals into ratings and issue counts.

Release testing

Run full E2E, regression, performance, compatibility, static analysis and spec validation on every release branch.

AI fix workflow

For blockers, the agent drafts plan, branch, implementation, validation and pull request while humans approve each gate.

Enterprise deployment

Keep data inside the client's perimeter

The ABB deck emphasized a deployment where AI runs through the client's AWS account. This product page keeps that as an install option for regulated enterprise environments.

Developer workstationEngineer works inside the client office network or VPN with controlled access to repositories and tooling.

Claude Code via BedrockConfigured through AWS IAM with no Anthropic API keys and no direct public Anthropic calls.

VPC PrivateLinkInterface endpoint inside the client's VPC keeps inference traffic on the AWS backbone.

KMS and CloudTrailKMS encryption at rest, TLS in transit and every API call logged for audit review.

Compliance postureAWS Bedrock compliance baseline plus S-PRO ISO 27001 and ISO 27701 delivery governance.

Pilot roadmap

From anchor system to maintainable operating model

The product can start with one anchor system, run a controlled pilot and hand over runbooks so the client can operate it with a lean team.

Anchor system

Scope workshop, identify anchor system and agree success criteria.

Set up

Provision VPC, IAM, AI testing layer, Bedrock and PrivateLink.

Onboard

Upload specs, code and tests; build platform breakdown and automate manual tests.

W4+

Run and handover

Run health monitoring, release testing, fix workflow and weekly reviews, then train the team.

Buyer pack

What the client needs before installing it

Legacy optimization only sells when the buyer can see operational impact, security boundaries, onboarding effort and measurable outcomes.

Inputs required

Anchor system repository and dependency inventory
Manual regression scripts, test cases and business sign-off flow
Functional specs, Confluence pages, ADRs and Jira history
CI/CD access, staging environment and release branch pattern

Outputs delivered

Module decomposition and fit-gap coverage map
Health dashboard with blockers, vulnerabilities and debt
Automated release-testing suite and blocker workflow
Runbooks, handover training and grace-period support

Expected pilot outcome from the deck: the system can be maintained by one FTE once runbooks, monitoring and workflows are handed over.

Commercial outcomes

Why legacy teams buy modernization

Less manual testingReduce repetitive click-through regression work every release.

Safer releasesCatch blockers, coverage gaps and spec drift before production rollout.

Lower debtPrioritize vulnerabilities, complexity and outdated dependencies by module.

Lean opsMove toward a maintained-by-1-FTE operating model after the pilot.

Delivered examples

Modernizing around the core: delivered examples

Modernization rarely means replacing the core. These projects added an API layer and new channels around core systems that stayed in operation. Across 350+ projects in 33 countries, 55% for fintech, and 26 private banks, the pattern is the same: keep the system of record, build the API and experience layers around it, and add AI agents where they remove manual work. See AI agents for banks.

AMINA (Swiss-regulated bank)

A new interaction architecture towards the core banking system, an intermediate API layer and a new web and mobile client experience. Case study

New Horizon Bank (US community bank)

A B2B banking platform on top of the legacy DCI core through an integration API layer; in production since December 2025; three payment workflows (wire, ACH, internal transfers). Case study

Investec

Onboarding process redesigned within the existing banking infrastructure: 60 to 37 KYC touchpoints, 38% fewer steps.

Sygnum

One integration platform for 20+ partner banks.

Questions

Core and legacy modernization: questions buyers ask

Do we have to replace our core banking system to modernize?

No. At New Horizon Bank the platform was built on top of the existing DCI core through an integration API layer, and at AMINA an intermediate API layer connects the new web and mobile experience to the core system. Replacement is a separate decision from modernization.

Can the AI run inside our own environment?

Yes. As an install option for regulated environments, the AI runs through the client's AWS account: Claude Code via Amazon Bedrock with IAM (no Anthropic API keys), VPC PrivateLink, KMS encryption and CloudTrail logging, under an ISO 27001 / ISO 27701 governance baseline.

How do we start?

With a design thinking pilot from USD 20k, 2–4 weeks, which defines the anchor system, the use case, the architecture and the path to production.

Turn legacy maintenance into a governed product.

Start with one anchor system, prove the modernization workflow, then expand to the systems where manual regression burns the most budget.

Book a Legacy Optimization session Return to S-PRO fintech